In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
In [ ]:
In [2]:
df1=pd.read_csv(r'F:\ipl\deliveries.csv')
In [3]:
df1.head(5)
Out[3]:
match_id inning batting_team bowling_team over ball batter bowler non_striker batsman_runs extra_runs total_runs extras_type is_wicket player_dismissed dismissal_kind fielder
0 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 1 SC Ganguly P Kumar BB McCullum 0 1 1 legbyes 0 NaN NaN NaN
1 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 2 BB McCullum P Kumar SC Ganguly 0 0 0 NaN 0 NaN NaN NaN
2 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 3 BB McCullum P Kumar SC Ganguly 0 1 1 wides 0 NaN NaN NaN
3 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 4 BB McCullum P Kumar SC Ganguly 0 0 0 NaN 0 NaN NaN NaN
4 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 5 BB McCullum P Kumar SC Ganguly 0 0 0 NaN 0 NaN NaN NaN
In [4]:
df1.columns
Out[4]:
Index(['match_id', 'inning', 'batting_team', 'bowling_team', 'over', 'ball',
       'batter', 'bowler', 'non_striker', 'batsman_runs', 'extra_runs',
       'total_runs', 'extras_type', 'is_wicket', 'player_dismissed',
       'dismissal_kind', 'fielder'],
      dtype='object')
In [5]:
df2=pd.read_csv(r'F:\ipl\matches.csv')
In [6]:
df2.columns
Out[6]:
Index(['id', 'season', 'city', 'date', 'match_type', 'player_of_match',
       'venue', 'team1', 'team2', 'toss_winner', 'toss_decision', 'winner',
       'result', 'result_margin', 'target_runs', 'target_overs', 'super_over',
       'method', 'umpire1', 'umpire2'],
      dtype='object')
In [7]:
df2.head(10)
Out[7]:
id season city date match_type player_of_match venue team1 team2 toss_winner toss_decision winner result result_margin target_runs target_overs super_over method umpire1 umpire2
0 335982 2007/08 Bangalore 2008-04-18 League BB McCullum M Chinnaswamy Stadium Royal Challengers Bangalore Kolkata Knight Riders Royal Challengers Bangalore field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
1 335983 2007/08 Chandigarh 2008-04-19 League MEK Hussey Punjab Cricket Association Stadium, Mohali Kings XI Punjab Chennai Super Kings Chennai Super Kings bat Chennai Super Kings runs 33.0 241.0 20.0 N NaN MR Benson SL Shastri
2 335984 2007/08 Delhi 2008-04-19 League MF Maharoof Feroz Shah Kotla Delhi Daredevils Rajasthan Royals Rajasthan Royals bat Delhi Daredevils wickets 9.0 130.0 20.0 N NaN Aleem Dar GA Pratapkumar
3 335985 2007/08 Mumbai 2008-04-20 League MV Boucher Wankhede Stadium Mumbai Indians Royal Challengers Bangalore Mumbai Indians bat Royal Challengers Bangalore wickets 5.0 166.0 20.0 N NaN SJ Davis DJ Harper
4 335986 2007/08 Kolkata 2008-04-20 League DJ Hussey Eden Gardens Kolkata Knight Riders Deccan Chargers Deccan Chargers bat Kolkata Knight Riders wickets 5.0 111.0 20.0 N NaN BF Bowden K Hariharan
5 335987 2007/08 Jaipur 2008-04-21 League SR Watson Sawai Mansingh Stadium Rajasthan Royals Kings XI Punjab Kings XI Punjab bat Rajasthan Royals wickets 6.0 167.0 20.0 N NaN Aleem Dar RB Tiffin
6 335988 2007/08 Hyderabad 2008-04-22 League V Sehwag Rajiv Gandhi International Stadium, Uppal Deccan Chargers Delhi Daredevils Deccan Chargers bat Delhi Daredevils wickets 9.0 143.0 20.0 N NaN IL Howell AM Saheba
7 335989 2007/08 Chennai 2008-04-23 League ML Hayden MA Chidambaram Stadium, Chepauk Chennai Super Kings Mumbai Indians Mumbai Indians field Chennai Super Kings runs 6.0 209.0 20.0 N NaN DJ Harper GA Pratapkumar
8 335990 2007/08 Hyderabad 2008-04-24 League YK Pathan Rajiv Gandhi International Stadium, Uppal Deccan Chargers Rajasthan Royals Rajasthan Royals field Rajasthan Royals wickets 3.0 215.0 20.0 N NaN Asad Rauf MR Benson
9 335991 2007/08 Chandigarh 2008-04-25 League KC Sangakkara Punjab Cricket Association Stadium, Mohali Kings XI Punjab Mumbai Indians Mumbai Indians field Kings XI Punjab runs 66.0 183.0 20.0 N NaN Aleem Dar AM Saheba
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In [8]:
df2.shape
Out[8]:
(1095, 20)

HANDLING MISSING VALUES¶

In [9]:
df2.isnull().sum()
Out[9]:
id                    0
season                0
city                 51
date                  0
match_type            0
player_of_match       5
venue                 0
team1                 0
team2                 0
toss_winner           0
toss_decision         0
winner                5
result                0
result_margin        19
target_runs           3
target_overs          3
super_over            0
method             1074
umpire1               0
umpire2               0
dtype: int64
In [10]:
df2['method'].unique()
Out[10]:
array([nan, 'D/L'], dtype=object)
In [11]:
df2['method'].fillna('No method applied',inplace=True)
In [12]:
df2['target_overs'].fillna(df2['target_overs'].median(),inplace=True)
In [13]:
df2['target_runs'].fillna(df2['target_runs'].median(),inplace=True)
In [14]:
df2['result_margin'].fillna(df2['result_margin'].median(),inplace=True)
In [15]:
df2['city'].unique()
Out[15]:
array(['Bangalore', 'Chandigarh', 'Delhi', 'Mumbai', 'Kolkata', 'Jaipur',
       'Hyderabad', 'Chennai', 'Cape Town', 'Port Elizabeth', 'Durban',
       'Centurion', 'East London', 'Johannesburg', 'Kimberley',
       'Bloemfontein', 'Ahmedabad', 'Cuttack', 'Nagpur', 'Dharamsala',
       'Kochi', 'Indore', 'Visakhapatnam', 'Pune', 'Raipur', 'Ranchi',
       'Abu Dhabi', nan, 'Rajkot', 'Kanpur', 'Bengaluru', 'Dubai',
       'Sharjah', 'Navi Mumbai', 'Lucknow', 'Guwahati', 'Mohali'],
      dtype=object)
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assigning the city name according the venue name¶

In [16]:
df2[df2['city'].isnull()]['venue'].unique()
Out[16]:
array(['Sharjah Cricket Stadium', 'Dubai International Cricket Stadium'],
      dtype=object)
In [17]:
df2.loc[df2['venue']=='Sharjah Cricket Stadium','city']='Sharjah'
df2.loc[df2['venue']=='Dubai International Cricket Stadium','city']='Dubai'
In [18]:
df2['winner'].fillna('No Result', inplace=True)
df2['player_of_match'].fillna('No Result',inplace=True)
In [19]:
df2.isnull().sum()
Out[19]:
id                 0
season             0
city               0
date               0
match_type         0
player_of_match    0
venue              0
team1              0
team2              0
toss_winner        0
toss_decision      0
winner             0
result             0
result_margin      0
target_runs        0
target_overs       0
super_over         0
method             0
umpire1            0
umpire2            0
dtype: int64
In [ ]:
In [20]:
df2['venue'].value_counts()
Out[20]:
Eden Gardens                                                             77
Wankhede Stadium                                                         73
M Chinnaswamy Stadium                                                    65
Feroz Shah Kotla                                                         60
Rajiv Gandhi International Stadium, Uppal                                49
MA Chidambaram Stadium, Chepauk                                          48
Sawai Mansingh Stadium                                                   47
Dubai International Cricket Stadium                                      46
Wankhede Stadium, Mumbai                                                 45
Punjab Cricket Association Stadium, Mohali                               35
Sheikh Zayed Stadium                                                     29
Sharjah Cricket Stadium                                                  28
MA Chidambaram Stadium, Chepauk, Chennai                                 28
Narendra Modi Stadium, Ahmedabad                                         24
Maharashtra Cricket Association Stadium                                  22
Dr DY Patil Sports Academy, Mumbai                                       20
Brabourne Stadium, Mumbai                                                17
Dr DY Patil Sports Academy                                               17
Eden Gardens, Kolkata                                                    16
Subrata Roy Sahara Stadium                                               16
Arun Jaitley Stadium, Delhi                                              16
Rajiv Gandhi International Stadium                                       15
M.Chinnaswamy Stadium                                                    15
Kingsmead                                                                15
Arun Jaitley Stadium                                                     14
Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow    14
M Chinnaswamy Stadium, Bengaluru                                         14
Rajiv Gandhi International Stadium, Uppal, Hyderabad                     13
Maharashtra Cricket Association Stadium, Pune                            13
Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium                      13
SuperSport Park                                                          12
Sardar Patel Stadium, Motera                                             12
Punjab Cricket Association IS Bindra Stadium, Mohali                     11
Sawai Mansingh Stadium, Jaipur                                           10
Saurashtra Cricket Association Stadium                                   10
Brabourne Stadium                                                        10
Punjab Cricket Association IS Bindra Stadium                             10
Himachal Pradesh Cricket Association Stadium                              9
MA Chidambaram Stadium                                                    9
Holkar Cricket Stadium                                                    9
New Wanderers Stadium                                                     8
Zayed Cricket Stadium, Abu Dhabi                                          8
JSCA International Stadium Complex                                        7
Barabati Stadium                                                          7
St George's Park                                                          7
Newlands                                                                  7
Shaheed Veer Narayan Singh International Stadium                          6
Nehru Stadium                                                             5
Punjab Cricket Association IS Bindra Stadium, Mohali, Chandigarh          5
Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur        5
Green Park                                                                4
Himachal Pradesh Cricket Association Stadium, Dharamsala                  4
Vidarbha Cricket Association Stadium, Jamtha                              3
De Beers Diamond Oval                                                     3
Buffalo Park                                                              3
Barsapara Cricket Stadium, Guwahati                                       3
OUTsurance Oval                                                           2
Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium, Visakhapatnam        2
Name: venue, dtype: int64

Changes the stadium names correctly such as 'Eden Gardens' and 'Eden Gardens, Kolkata' is same stadium¶

In [21]:
df2[df2['venue'].str.startswith('Eden')]['venue'].unique()
Out[21]:
array(['Eden Gardens', 'Eden Gardens, Kolkata'], dtype=object)
In [22]:
df2['venue']=df2['venue'].str.replace('Eden Gardens, Kolkata','Eden Gardens')
In [23]:
df2[df2['venue'].str.startswith('Wankhede')]['venue'].unique()
Out[23]:
array(['Wankhede Stadium', 'Wankhede Stadium, Mumbai'], dtype=object)
In [24]:
df2['venue']=df2['venue'].str.replace('Wankhede Stadium, Mumbai','Wankhede Stadium')
In [25]:
df2[df2['venue'].str.startswith('M Chinnaswamy')]['venue'].unique()
Out[25]:
array(['M Chinnaswamy Stadium', 'M Chinnaswamy Stadium, Bengaluru'],
      dtype=object)
In [26]:
df2['venue']=df2['venue'].str.replace('M Chinnaswamy Stadium, Bengaluru','M Chinnaswamy Stadium')
In [27]:
df2['venue']=df2['venue'].replace('M.Chinnaswamy Stadium','M Chinnaswamy Stadium')
In [28]:
df2[df2['venue'].str.startswith('Rajiv')]['venue'].unique()
Out[28]:
array(['Rajiv Gandhi International Stadium, Uppal',
       'Rajiv Gandhi International Stadium',
       'Rajiv Gandhi International Stadium, Uppal, Hyderabad'],
      dtype=object)
In [29]:
df2['venue']=df2['venue'].replace(['Rajiv Gandhi International Stadium, Uppal',
        'Rajiv Gandhi International Stadium, Uppal, Hyderabad'],'Rajiv Gandhi International Stadium')
In [ ]:
In [30]:
df2[df2['venue'].str.startswith('MA Chidambaram')]['venue'].unique()
Out[30]:
array(['MA Chidambaram Stadium, Chepauk', 'MA Chidambaram Stadium',
       'MA Chidambaram Stadium, Chepauk, Chennai'], dtype=object)
In [31]:
df2['venue']=df2['venue'].replace(['MA Chidambaram Stadium, Chepauk',
        'MA Chidambaram Stadium, Chepauk, Chennai'],'MA Chidambaram Stadium')
In [ ]:
In [32]:
df2[df2['venue'].str.startswith('Sawai')]['venue'].unique()
Out[32]:
array(['Sawai Mansingh Stadium', 'Sawai Mansingh Stadium, Jaipur'],
      dtype=object)
In [33]:
df2['venue']=df2['venue'].str.replace('Sawai Mansingh Stadium, Jaipur','Sawai Mansingh Stadium')
In [ ]:
In [34]:
df2[df2['venue'].str.startswith('Punjab')]['venue'].unique()
Out[34]:
array(['Punjab Cricket Association Stadium, Mohali',
       'Punjab Cricket Association IS Bindra Stadium, Mohali',
       'Punjab Cricket Association IS Bindra Stadium',
       'Punjab Cricket Association IS Bindra Stadium, Mohali, Chandigarh'],
      dtype=object)
In [35]:
df2['venue']=df2['venue'].replace(['Punjab Cricket Association Stadium, Mohali',
       'Punjab Cricket Association IS Bindra Stadium, Mohali',
       'Punjab Cricket Association IS Bindra Stadium, Mohali, Chandigarh'],'Punjab Cricket Association IS Bindra Stadium')
In [ ]:
In [ ]:
In [36]:
df2[df2['venue'].str.startswith('Maharashtra')]['venue'].unique()
Out[36]:
array(['Maharashtra Cricket Association Stadium',
       'Maharashtra Cricket Association Stadium, Pune'], dtype=object)
In [37]:
df2['venue']=df2['venue'].str.replace('Maharashtra Cricket Association Stadium, Pune','Maharashtra Cricket Association Stadium')
In [ ]:
In [38]:
df2[df2['venue'].str.startswith('Dr DY')]['venue'].unique()
Out[38]:
array(['Dr DY Patil Sports Academy', 'Dr DY Patil Sports Academy, Mumbai'],
      dtype=object)
In [39]:
df2['venue']=df2['venue'].str.replace('Dr DY Patil Sports Academy, Mumbai','Dr DY Patil Sports Academy')
In [ ]:
In [40]:
df2[df2['venue'].str.startswith('Brabourne')]['venue'].unique()
Out[40]:
array(['Brabourne Stadium', 'Brabourne Stadium, Mumbai'], dtype=object)
In [41]:
df2['venue']=df2['venue'].str.replace('Brabourne Stadium, Mumbai','Brabourne Stadium')
In [ ]:
In [42]:
df2[df2['venue'].str.startswith('Arun Jaitley')]['venue'].unique()
Out[42]:
array(['Arun Jaitley Stadium', 'Arun Jaitley Stadium, Delhi'],
      dtype=object)
In [43]:
df2['venue']=df2['venue'].str.replace('Arun Jaitley Stadium, Delhi','Arun Jaitley Stadium')
In [44]:
df2['venue']=df2['venue'].str.replace('Feroz Shah Kotla','Arun Jaitley Stadium')
In [ ]:
In [45]:
df2[df2['venue'].str.startswith('Dr. Y.S.')]['venue'].unique()
Out[45]:
array(['Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium',
       'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium, Visakhapatnam'],
      dtype=object)
In [46]:
df2['venue']=df2['venue'].str.replace('Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium, Visakhapatnam','Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium',regex=False)
In [ ]:
In [47]:
df2[df2['venue'].str.startswith('Himachal')]['venue'].unique()
Out[47]:
array(['Himachal Pradesh Cricket Association Stadium',
       'Himachal Pradesh Cricket Association Stadium, Dharamsala'],
      dtype=object)
In [48]:
df2['venue']=df2['venue'].str.replace('Himachal Pradesh Cricket Association Stadium, Dharamsala','Himachal Pradesh Cricket Association Stadium')
In [ ]:
In [ ]:

Q.1 ---> FIND THE STADIUM WHERE THE MOST MATCH PLAYED (2008-2024)¶

In [49]:
Stadium=df2['venue'].value_counts().reset_index()
Stadium.columns=['Stadium','Number of Matches']
In [50]:
Stadium.style.background_gradient(cmap='viridis')
Out[50]:
  Stadium Number of Matches
0 Wankhede Stadium 118
1 M Chinnaswamy Stadium 94
2 Eden Gardens 93
3 Arun Jaitley Stadium 90
4 MA Chidambaram Stadium 85
5 Rajiv Gandhi International Stadium 77
6 Punjab Cricket Association IS Bindra Stadium 61
7 Sawai Mansingh Stadium 57
8 Dubai International Cricket Stadium 46
9 Dr DY Patil Sports Academy 37
10 Maharashtra Cricket Association Stadium 35
11 Sheikh Zayed Stadium 29
12 Sharjah Cricket Stadium 28
13 Brabourne Stadium 27
14 Narendra Modi Stadium, Ahmedabad 24
15 Subrata Roy Sahara Stadium 16
16 Kingsmead 15
17 Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium 15
18 Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 14
19 Himachal Pradesh Cricket Association Stadium 13
20 SuperSport Park 12
21 Sardar Patel Stadium, Motera 12
22 Saurashtra Cricket Association Stadium 10
23 Holkar Cricket Stadium 9
24 New Wanderers Stadium 8
25 Zayed Cricket Stadium, Abu Dhabi 8
26 JSCA International Stadium Complex 7
27 Barabati Stadium 7
28 St George's Park 7
29 Newlands 7
30 Shaheed Veer Narayan Singh International Stadium 6
31 Nehru Stadium 5
32 Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur 5
33 Green Park 4
34 Vidarbha Cricket Association Stadium, Jamtha 3
35 De Beers Diamond Oval 3
36 Buffalo Park 3
37 Barsapara Cricket Stadium, Guwahati 3
38 OUTsurance Oval 2

CONCLUSION ->>¶

TILL NOW Wankhede Stadium HOLDS THE HIGHEST NUMBER OF MATCH PLAYED¶

In [ ]:
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Q.2 ---> TOTAL RUNS ACCROSS SEASON¶

In [ ]:
In [51]:
season_data=df2[['id','season']].merge(df1,left_on='id',right_on='match_id',how='left').drop('match_id',axis=1)
In [52]:
season=season_data.groupby('season')['total_runs'].sum().reset_index()
In [53]:
p=season.set_index('season')
In [54]:
fig=px.line(p,x=p.index,y='total_runs',title='TOTAL RUNS ACCROSS SEASON',labels={'total_runs':'TOTAL RUNS'})
fig.update_layout(template='simple_white',title_font={'size':26})
fig.show()
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CONCLUSION --> DAY BY DAY THE RUNS BUBBLE IN IPL INCREASING¶

In [ ]:
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Q.3 ---> UMPIRE WITH MOST NUMBER OF UMPIRING¶

In [ ]:
In [ ]:
In [55]:
ump=pd.concat([df2['umpire1'],df2['umpire2']])
In [56]:
ump=ump.value_counts().reset_index()
In [57]:
ump.columns=['Name','Number of Matches']
In [58]:
ump.style.background_gradient(cmap='viridis')
Out[58]:
  Name Number of Matches
0 S Ravi 131
1 AK Chaudhary 131
2 Nitin Menon 104
3 HDPK Dharmasena 98
4 C Shamshuddin 88
5 CB Gaffaney 77
6 KN Ananthapadmanabhan 72
7 M Erasmus 67
8 VK Sharma 65
9 BNJ Oxenford 64
10 RJ Tucker 62
11 CK Nandan 59
12 VA Kulkarni 57
13 SJA Taufel 55
14 Asad Rauf 51
15 BR Doctrove 42
16 YC Barde 42
17 RE Koertzen 41
18 RK Illingworth 39
19 Aleem Dar 38
20 NJ Llong 37
21 BF Bowden 37
22 A Nand Kishore 37
23 AY Dandekar 32
24 UV Gandhe 31
25 SK Tarapore 31
26 S Asnani 30
27 RB Tiffin 30
28 PR Reiffel 28
29 DJ Harper 27
30 AM Saheba 26
31 J Madanagopal 26
32 R Pandit 22
33 MA Gough 21
34 K Hariharan 21
35 Tapan Sharma 20
36 NA Patwardhan 20
37 Navdeep Singh 19
38 HAS Khalid 18
39 SS Hazare 17
40 BG Jerling 17
41 JD Cloete 17
42 PG Pathak 16
43 MV Saidharshan Kumar 15
44 S Das 15
45 K Srinath 15
46 IL Howell 14
47 Vinod Seshan 14
48 SL Shastri 14
49 A Totre 13
50 GR Sadashiv Iyer 12
51 A Deshmukh 12
52 MR Benson 12
53 K Srinivasan 9
54 SJ Davis 7
55 GAV Baxter 7
56 RM Deshpande 7
57 Chirra Ravikanthreddy 6
58 AL Hill 6
59 I Shivram 6
60 GA Pratapkumar 6
61 AV Jayaprakash 6
62 SD Fry 6
63 IJ Gould 6
64 N Pandit 6
65 TH Wijewardene 5
66 K Bharatan 3
67 AG Wharf 2
68 SD Ranade 2
69 Subroto Das 1
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CONCLUSION -- S RAVI AND AK CHAUDHARY HAS UMPAIRED IN 131 MATCHES ,FOLLOWED BY NITIN MENON WHO HAS UMPAIRED 104 MATCHES¶

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Q.3 ---> MOST NUMBER OF TOSS WINNER IN THE IPL SEASON¶

In [ ]:
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IDENTIFY AND REPLACE THE STADIUM NAME WHICH IS SAME SUCH AS 'Rising Pune Supergiants' AND 'Rising Pune Supergiant' BOTH ARE SAME¶

'Delhi Daredevils' AND 'Delhi Capitals' BOTH ARE SAME TEAM BUT CHANGES THEIR NAME¶

In [ ]:
In [ ]:
In [59]:
df2[df2['toss_winner'].str.startswith('Rising Pune')]['toss_winner'].unique()
Out[59]:
array(['Rising Pune Supergiants', 'Rising Pune Supergiant'], dtype=object)
In [60]:
df2['toss_winner']=df2['toss_winner'].str.replace('Rising Pune Supergiants','Rising Pune Supergiant')
In [61]:
df2[df2['toss_winner'].str.startswith('Royal')]['toss_winner'].unique()
Out[61]:
array(['Royal Challengers Bangalore', 'Royal Challengers Bengaluru'],
      dtype=object)
In [62]:
df2['toss_winner']=df2['toss_winner'].str.replace('Royal Challengers Bengaluru','Royal Challengers Bangalore')
In [63]:
df2[df2['toss_winner'].str.startswith('Delhi')]['toss_winner'].unique()
Out[63]:
array(['Delhi Daredevils', 'Delhi Capitals'], dtype=object)
In [64]:
df2['toss_winner']=df2['toss_winner'].str.replace('Delhi Daredevils','Delhi Capitals')
In [65]:
df2['toss_winner']=df2['toss_winner'].str.replace('Kings XI Punjab','Punjab Kings')
In [ ]:
In [ ]:
In [66]:
df2['toss_winner'].value_counts()
Out[66]:
Mumbai Indians                 143
Delhi Capitals                 130
Chennai Super Kings            122
Kolkata Knight Riders          122
Royal Challengers Bangalore    121
Rajasthan Royals               120
Punjab Kings                   109
Sunrisers Hyderabad             88
Deccan Chargers                 43
Gujarat Titans                  22
Pune Warriors                   20
Lucknow Super Giants            19
Gujarat Lions                   15
Rising Pune Supergiant          13
Kochi Tuskers Kerala             8
Name: toss_winner, dtype: int64
In [ ]:
In [67]:
toss=df2['toss_winner'].value_counts().reset_index()
toss.columns=['toss_winner','count']
In [68]:
fig=px.bar(toss,x='toss_winner',y='count',title='Total Toss Winner',color_discrete_sequence=['#40E0D0'])
fig.update_layout(template='simple_white',width=800, 
    height=400)
fig.show()
In [ ]:
In [ ]:

CONCLUSION -- Mumbai Indians HAS WON THE HIGHEST NUMBER OF TOSS (143) , FOLLOWED BY DELHI CAPITALS (130)¶

In [ ]:
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In [ ]:

Q.4 --> Which decision was made after winning the toss¶

In [ ]:
In [ ]:
In [69]:
toss_series=df2.toss_decision.value_counts()
In [70]:
toss_series
Out[70]:
field    704
bat      391
Name: toss_decision, dtype: int64
In [71]:
labels=(np.array(toss_series.index))
labels
Out[71]:
array(['field', 'bat'], dtype=object)
In [72]:
values=(np.array(toss_series/toss_series.sum())*100)
In [73]:
values
Out[73]:
array([64.29223744, 35.70776256])
In [74]:
fig=go.Figure(

    data=go.Pie(
    
    labels=labels,values=values,hole=.3
        
    )
)

fig.update_traces(hoverinfo='label+percent',textinfo='label+percent')
fig.update_layout(title='Decission Made After Toss Winning')
fig.show()
In [ ]:
In [ ]:

CONCLUSION --- ITS A TENDS TO CHOOSE FIELD FIRST AFTER WINNING THE TOSS¶

In [ ]:
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Q.5 ---> VENUE WISE TOSS WIN MATCH WIN PERCENTAGE¶

In [ ]:
In [ ]:
In [ ]:
In [75]:
df2['toss_winner_won']= (df2['toss_winner']==df2['winner'])
In [76]:
result=df2.groupby('venue',as_index=False).agg(

    total_match=('id','count'),
    toss_winner_matches=('toss_winner_won','sum')
).sort_values(by='total_match',ascending=False)
In [ ]:
In [77]:
result['toss_win_match_win_percentage'] = (result['toss_winner_matches'] / result['total_match']) * 100

result=result.reset_index(drop=1)
result.style.background_gradient(cmap='viridis')
Out[77]:
  venue total_match toss_winner_matches toss_win_match_win_percentage
0 Wankhede Stadium 118 60 50.847458
1 M Chinnaswamy Stadium 94 42 44.680851
2 Eden Gardens 93 47 50.537634
3 Arun Jaitley Stadium 90 29 32.222222
4 MA Chidambaram Stadium 85 41 48.235294
5 Rajiv Gandhi International Stadium 77 21 27.272727
6 Punjab Cricket Association IS Bindra Stadium 61 15 24.590164
7 Sawai Mansingh Stadium 57 30 52.631579
8 Dubai International Cricket Stadium 46 17 36.956522
9 Dr DY Patil Sports Academy 37 19 51.351351
10 Maharashtra Cricket Association Stadium 35 19 54.285714
11 Sheikh Zayed Stadium 29 15 51.724138
12 Sharjah Cricket Stadium 28 14 50.000000
13 Brabourne Stadium 27 14 51.851852
14 Narendra Modi Stadium, Ahmedabad 24 11 45.833333
15 Subrata Roy Sahara Stadium 16 9 56.250000
16 Kingsmead 15 7 46.666667
17 Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium 15 6 40.000000
18 Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 14 8 57.142857
19 Himachal Pradesh Cricket Association Stadium 13 3 23.076923
20 SuperSport Park 12 6 50.000000
21 Sardar Patel Stadium, Motera 12 4 33.333333
22 Saurashtra Cricket Association Stadium 10 3 30.000000
23 Holkar Cricket Stadium 9 4 44.444444
24 Zayed Cricket Stadium, Abu Dhabi 8 4 50.000000
25 New Wanderers Stadium 8 2 25.000000
26 Newlands 7 2 28.571429
27 St George's Park 7 3 42.857143
28 Barabati Stadium 7 5 71.428571
29 JSCA International Stadium Complex 7 4 57.142857
30 Shaheed Veer Narayan Singh International Stadium 6 2 33.333333
31 Nehru Stadium 5 1 20.000000
32 Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur 5 2 40.000000
33 Green Park 4 3 75.000000
34 De Beers Diamond Oval 3 1 33.333333
35 Vidarbha Cricket Association Stadium, Jamtha 3 1 33.333333
36 Barsapara Cricket Stadium, Guwahati 3 0 0.000000
37 Buffalo Park 3 2 66.666667
38 OUTsurance Oval 2 0 0.000000
In [ ]:
In [ ]:

Conclusion: From the data, we can clearly see that at Wankhede Stadium, a total of 118 matches were played, out of which 60 were won by teams that won the toss. This gives a toss-win match-win percentage of 50.84%, which is the highest. The second highest is at M. Chinnaswamy Stadium, with a toss-win match-win percentage of 44.68%.¶

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In [ ]:
In [ ]:

Q.6 --> TEAM'S WON THE TOURNAMENT¶

In [ ]:
In [ ]:
In [ ]:
In [78]:
winners_ipl=df2.groupby('season')['winner'].last().reset_index(name='team')
In [79]:
winners_ipl
Out[79]:
season team
0 2007/08 Rajasthan Royals
1 2009 Deccan Chargers
2 2009/10 Chennai Super Kings
3 2011 Chennai Super Kings
4 2012 Kolkata Knight Riders
5 2013 Mumbai Indians
6 2014 Kolkata Knight Riders
7 2015 Mumbai Indians
8 2016 Sunrisers Hyderabad
9 2017 Mumbai Indians
10 2018 Chennai Super Kings
11 2019 Mumbai Indians
12 2020/21 Mumbai Indians
13 2021 Chennai Super Kings
14 2022 Gujarat Titans
15 2023 Chennai Super Kings
16 2024 Kolkata Knight Riders
In [80]:
team_win=winners_ipl['team'].value_counts()
In [81]:
team_wins=team_win.reset_index()
In [82]:
team_wins.columns=['team','wins']
In [83]:
team_wins.style.background_gradient(cmap='Blues')
Out[83]:
  team wins
0 Chennai Super Kings 5
1 Mumbai Indians 5
2 Kolkata Knight Riders 3
3 Rajasthan Royals 1
4 Deccan Chargers 1
5 Sunrisers Hyderabad 1
6 Gujarat Titans 1
In [ ]:
In [84]:
colors=['crimson']*2+['turquoise']*(len(team_wins)-1)
fig=go.Figure(
data=[
  go.Bar( x=team_wins['team'],y=team_wins['wins'],marker_color=colors,width=0.2)
])

fig.update_layout(

    title='IPL Winning Teams',
    xaxis_title='Team',
    yaxis_title='Win',
    bargap=0,
    width=600
)
fig.show()
In [ ]:
In [ ]:

CONCLUSION --- Chennai Super Kings WON THE MOST TOURAMENT TITLE 5 TIMES ALONG WITH THE Mumbai Indians, FOLLOWED BY Kolkata Knight Riders WITH 3 TROPHY¶

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In [ ]:

Q.7 ---> FIND THE TOTAL MATCHS AND WIN MATCHES BY EACH TEAM¶

In [ ]:
In [ ]:

replace the values where it same such as 'Delhi Daredevils' and 'Delhi Capitals' both are same team they just change their name, 'Royal Challengers Bengaluru','Royal Challengers Bangalore' same team ....¶

In [ ]:
In [ ]:
In [85]:
df2['winner']=df2['winner'].str.replace('Kings XI Punjab','Punjab Kings')
In [86]:
df2['winner']=df2['winner'].str.replace('Delhi Daredevils','Delhi Capitals')
In [87]:
df2['winner']=df2['winner'].str.replace('Royal Challengers Bengaluru','Royal Challengers Bangalore')
In [88]:
df2['winner']=df2['winner'].str.replace('Rising Pune Supergiants','Rising Pune Supergiant')
In [89]:
data=df2['winner'].value_counts()
In [90]:
win_match=data.drop('No Result')
In [91]:
win_match
Out[91]:
Mumbai Indians                 144
Chennai Super Kings            138
Kolkata Knight Riders          131
Royal Challengers Bangalore    123
Delhi Capitals                 115
Rajasthan Royals               112
Punjab Kings                   112
Sunrisers Hyderabad             88
Deccan Chargers                 29
Gujarat Titans                  28
Lucknow Super Giants            24
Rising Pune Supergiant          15
Gujarat Lions                   13
Pune Warriors                   12
Kochi Tuskers Kerala             6
Name: winner, dtype: int64
In [ ]:
In [92]:
df2[['team1', 'team2']] = df2[['team1', 'team2']].apply(lambda x: x.str.replace('Rising Pune Supergiants', 'Rising Pune Supergiant'))
In [93]:
df2[['team1','team2']]=df2[['team1','team2']].apply(lambda x: x.str.replace('Royal Challengers Bengaluru','Royal Challengers Bangalore'))
In [94]:
df2[['team1','team2']]=df2[['team1','team2']].apply(lambda x: x.str.replace('Delhi Daredevils','Delhi Capitals'))
In [95]:
df2[['team1','team2']]=df2[['team1','team2']].apply(lambda x: x.str.replace('Kings XI Punjab','Punjab Kings'))
In [ ]:
In [ ]:
In [96]:
team=pd.concat([df2['team1'],df2['team2']])
In [97]:
total_matches = team.value_counts()
In [98]:
total_matches
Out[98]:
Mumbai Indians                 261
Royal Challengers Bangalore    255
Delhi Capitals                 252
Kolkata Knight Riders          251
Punjab Kings                   246
Chennai Super Kings            238
Rajasthan Royals               221
Sunrisers Hyderabad            182
Deccan Chargers                 75
Pune Warriors                   46
Gujarat Titans                  45
Lucknow Super Giants            44
Gujarat Lions                   30
Rising Pune Supergiant          30
Kochi Tuskers Kerala            14
dtype: int64
In [ ]:
In [99]:
team_played=pd.DataFrame(
{
    'Team':total_matches.index,
    'Total_Match':total_matches.values,
    'win_match':win_match.values
    
}
)
In [100]:
team_played.style.background_gradient(cmap='Blues')
Out[100]:
  Team Total_Match win_match
0 Mumbai Indians 261 144
1 Royal Challengers Bangalore 255 138
2 Delhi Capitals 252 131
3 Kolkata Knight Riders 251 123
4 Punjab Kings 246 115
5 Chennai Super Kings 238 112
6 Rajasthan Royals 221 112
7 Sunrisers Hyderabad 182 88
8 Deccan Chargers 75 29
9 Pune Warriors 46 28
10 Gujarat Titans 45 24
11 Lucknow Super Giants 44 15
12 Gujarat Lions 30 13
13 Rising Pune Supergiant 30 12
14 Kochi Tuskers Kerala 14 6
In [ ]:
In [101]:
team_w=team_played.head(12)
In [102]:
melted_df=team_w.melt(id_vars="Team",var_name="Match Type",value_name="Count")
In [103]:
fig=px.bar(

    melted_df,
    x="Team",
    y="Count",
    color="Match Type",
     title="TOTAL MATCHES VS WINS FOR EACH TEAM",
    barmode="group",
    labels={"Count": "Number of Matches", "Team": "IPL Teams"},
    height=500,
    width=700
    
)
fig.show()
In [ ]:
In [ ]:

CONCLUSION --- Mumbai Indians PLAYED 261 MATCHES AND WON 144 MATCHES, Royal Challengers Bangalore PLAYED 255 MATCHES AND WON 138 MATCHES...Delhi Capitals(252) and Kolkata Knight Riders(251) both played 250+ matches¶

In [ ]:
In [ ]:

Q.8 ---> LUCKY VENUE FOR A TEAM¶

In [ ]:
In [ ]:
In [ ]:
In [104]:
df2['team1'].value_counts()
Out[104]:
Royal Challengers Bangalore    144
Chennai Super Kings            128
Delhi Capitals                 126
Punjab Kings                   123
Mumbai Indians                 123
Kolkata Knight Riders          121
Rajasthan Royals               101
Sunrisers Hyderabad             86
Deccan Chargers                 39
Pune Warriors                   23
Lucknow Super Giants            23
Gujarat Titans                  21
Gujarat Lions                   16
Rising Pune Supergiant          14
Kochi Tuskers Kerala             7
Name: team1, dtype: int64
In [ ]:
In [105]:
def Lucky(match_data,team_name):
    return match_data[match_data['winner']==team_name]['venue'].value_counts().nlargest(10)
In [106]:
rcb=Lucky(df2,'Royal Challengers Bangalore')
values=rcb
labels=rcb.index
colors = ['turquoise', 'crimson']
fig=go.Figure(data=[go.Pie(labels=labels,values=values,hole=.2)])
fig.update_traces(hoverinfo='label+percent',textinfo='value')
fig.show()
In [ ]:
In [107]:
mi=Lucky(df2,'Mumbai Indians')
values=mi
labels=mi.index
colors = ['turquoise', 'crimson']
fig=go.Figure(data=[go.Pie(labels=labels,values=values,hole=.2)])
fig.update_traces(hoverinfo='label+percent',textinfo='value')
fig.show()
In [ ]:
In [ ]:
In [108]:
csk=Lucky(df2,'Chennai Super Kings')
values=csk
labels=csk.index
colors = ['turquoise', 'crimson']
fig=go.Figure(data=[go.Pie(labels=labels,values=values,hole=.2)])
fig.update_traces(hoverinfo='label+percent',textinfo='value')
fig.show()
In [ ]:
In [ ]:
In [109]:
dc=Lucky(df2,'Delhi Capitals')
values=dc
labels=dc.index
colors = ['turquoise', 'crimson']
fig=go.Figure(data=[go.Pie(labels=labels,values=values,hole=.2)])
fig.update_traces(hoverinfo='label+percent',textinfo='value')
fig.show()
In [ ]:
In [ ]:
In [110]:
pk=Lucky(df2,'Punjab Kings')
values=pk
labels=pk.index
colors = ['turquoise', 'crimson']
fig=go.Figure(data=[go.Pie(labels=labels,values=values,hole=.2)])
fig.update_traces(hoverinfo='label+percent',textinfo='value')
fig.show()
In [ ]:
In [111]:
k=Lucky(df2,'Kolkata Knight Riders')
values=k
labels=k.index
colors = ['turquoise', 'crimson']
fig=go.Figure(data=[go.Pie(labels=labels,values=values,hole=.2)])
fig.update_traces(hoverinfo='label+percent',textinfo='value')
fig.show()
In [ ]:
In [112]:
rr=Lucky(df2,'Rajasthan Royals')
values=rr
labels=rr.index
colors = ['turquoise', 'crimson']
fig=go.Figure(data=[go.Pie(labels=labels,values=values,hole=.2)])
fig.update_traces(hoverinfo='label+percent',textinfo='value')
fig.show()
In [ ]:
In [113]:
lsg=Lucky(df2,'Lucknow Super Giants')
values=lsg
labels=lsg.index
colors = ['turquoise', 'crimson']
fig=go.Figure(data=[go.Pie(labels=labels,values=values,hole=.2)])
fig.update_traces(hoverinfo='label+percent',textinfo='value')
fig.show()
In [ ]:
In [114]:
gt=Lucky(df2,'Gujarat Titans')
values=gt
labels=gt.index
colors = ['turquoise', 'crimson']
fig=go.Figure(data=[go.Pie(labels=labels,values=values,hole=.2)])
fig.update_traces(hoverinfo='label+percent',textinfo='value')
fig.show()
In [ ]:
In [115]:
srh=Lucky(df2,'Sunrisers Hyderabad')
values=srh
labels=srh.index
colors = ['turquoise', 'crimson']
fig=go.Figure(data=[go.Pie(labels=labels,values=values,hole=.2)])
fig.update_traces(hoverinfo='label+percent',textinfo='value')
fig.show()
In [ ]:
In [ ]:

CONCLUSION --- FROM HERE WE CAN COLCLUDE THAT EACH TEAMS FAVOURITE VENUE IS THEIR HOME GROUND¶

In [ ]:
In [ ]:
In [ ]:

Q.9 ---> FIND OUT EACH TEAM FIGHT HEAD TO HEAD¶

HERE WE COMPARE TEAM RCB WITH SOME OF other OPPONENT¶

In [ ]:
In [ ]:
In [116]:
def comparison (team1,team2):
    compare=df2[((df2['team1']==team1) | (df2['team2']==team1))   &  ((df2['team1']==team2) | (df2['team2']==team2))]
    group_data=compare.groupby('winner').size().reset_index(name='Match Win')
    fig=px.pie(group_data,names='winner',values='Match Win')
    fig.show()
In [117]:
comparison('Royal Challengers Bangalore','Mumbai Indians')
In [118]:
comparison('Royal Challengers Bangalore','Chennai Super Kings')
In [119]:
comparison('Royal Challengers Bangalore','Kolkata Knight Riders')
In [ ]:
In [ ]:
In [ ]:

Q.10 ---> TOP 5 PLAYERS WITH HIGHEST RUNS¶

In [ ]:
In [ ]:
In [120]:
df1['batting_team']=df1['batting_team'].str.replace('Kings XI Punjab','Punjab Kings')
In [121]:
df1['batting_team']=df1['batting_team'].str.replace('Rising Pune Supergiants','Rising Pune Supergiant')
In [122]:
df1['batting_team']=df1['batting_team'].str.replace('Royal Challengers Bengaluru','Royal Challengers Bangalore')
In [123]:
df1['batting_team']=df1['batting_team'].str.replace('Delhi Daredevils','Delhi Capitals')
In [ ]:
In [ ]:
In [124]:
df1['bowling_team']=df1['bowling_team'].str.replace('Kings XI Punjab','Punjab Kings')
In [125]:
df1['bowling_team']=df1['bowling_team'].str.replace('Rising Pune Supergiants','Rising Pune Supergiant')
In [126]:
df1['bowling_team']=df1['bowling_team'].str.replace('Royal Challengers Bengaluru','Royal Challengers Bangalore')
In [127]:
df1['bowling_team']=df1['bowling_team'].str.replace('Delhi Daredevils','Delhi Capitals')
In [ ]:
In [128]:
df1['batting_team'].value_counts()
Out[128]:
Mumbai Indians                 31437
Royal Challengers Bangalore    30023
Delhi Capitals                 29732
Kolkata Knight Riders          29514
Punjab Kings                   29479
Chennai Super Kings            28651
Rajasthan Royals               26242
Sunrisers Hyderabad            21843
Deccan Chargers                 9034
Gujarat Titans                  5494
Pune Warriors                   5443
Lucknow Super Giants            5400
Gujarat Lions                   3566
Rising Pune Supergiant          3480
Kochi Tuskers Kerala            1582
Name: batting_team, dtype: int64
In [ ]:
In [ ]:
In [129]:
top5=df1.groupby('batter',as_index=False)['batsman_runs'].sum().sort_values(by='batsman_runs',ascending=False).head(5)
In [ ]:
In [130]:
top5 = top5.reset_index(drop=True)
top5.style.background_gradient(cmap='Greens')
Out[130]:
  batter batsman_runs
0 V Kohli 8014
1 S Dhawan 6769
2 RG Sharma 6630
3 DA Warner 6567
4 SK Raina 5536
In [ ]:

KING KHOLI COMES WITH THE HIGHEST RUN SCORERS¶

In [ ]:

Q.11 ---> Individual player performance analysis: Here, I analyze Virat's performance.¶

In [ ]:
In [ ]:

Q.11A --> KOHLI'S RUN ANALYSIS¶

In [ ]:
In [131]:
virat=df1[df1['batter']=='V Kohli']
In [132]:
def run(df,runs):
    return len(df[df['batsman_runs']==runs])*runs
In [133]:
print(f"virat scored {run(virat,1)} by taking 1's ")
print(f"virat scored {run(virat,2)} by taking 2's ")
print(f"virat scored {run(virat,3)} by taking 3's ")
print(f"virat scored {run(virat,4)} by taking 4's ")
print(f"virat scored {run(virat,6)} by taking 6's ")

virat scored 2591 by taking 1's 
virat scored 890 by taking 2's 
virat scored 63 by taking 3's 
virat scored 2832 by taking 4's 
virat scored 1638 by taking 6's
In [134]:
total_runs=[2591,890,63,2832,1638]
shorts=[1,2,3,4,6]
fig=px.pie(values=total_runs,names=shorts)
fig.show()
In [ ]:
In [ ]:

VIRAT KOHLI RUN ANALYSIS HE SCORED scored 2591 by taking 1's (32.3%) , virat scored 890 by taking 2's(11.1%) ,HE scored 63 by taking 3's ,virat scored 2832 by hiting 4's (35.3%) AND scored 1638 by hiting 6's (20.4%),KING* LOVES TO TAKE SINGLE ALSO HE LOVE TO HIT 4'S*¶

In [ ]:
In [ ]:

Q.11B --> KHOLI'S DISMISSAL ANALYSIS¶

In [ ]:
In [ ]:
In [135]:
values=virat['dismissal_kind'].value_counts()
labels=virat['dismissal_kind'].value_counts().index
fig=go.Figure(data=[go.Pie(labels=labels,values=values,hole=0.3)]
             )
fig.update_traces(hoverinfo='label+percent',textinfo='label')
fig.update_layout(title='Virat dismissal in ipl')
fig.show()
In [ ]:
In [ ]:

MOST OF THE TIME HE CAUGHT OUT (63.3%) FOLLOWED BY BOWLED OUT(17.9%)¶

In [ ]:
In [ ]:

Q.11C --> WHO TAKES KOHLI'S WICKET MOST OF THE TIME¶

In [ ]:
In [ ]:
In [136]:
wicket_df=virat[virat['is_wicket']==1]
In [137]:
wicket=wicket_df.groupby('bowler').size().sort_values(ascending=False).head(5).reset_index(name='taken')
In [138]:
wicket.style.background_gradient(cmap='Blues')
Out[138]:
  bowler taken
0 Sandeep Sharma 7
1 A Nehra 6
2 RA Jadeja 5
3 UT Yadav 5
4 JJ Bumrah 5
In [ ]:
In [ ]:

7 TIMES Sandeep Sharma SEND HIM TO THE PAVILION ,FOLLOWED BY ASHISH NEHRA 6 TIMES¶

In [ ]:
In [ ]:

Q.11D --> KOHLI'S FIRST AND SEOCEND INNINGS COMPARISION¶

In [ ]:
In [ ]:
In [139]:
virat['inning'].value_counts()
Out[139]:
1    3473
2    2759
4       4
Name: inning, dtype: int64
In [140]:
virat = virat.copy()  # Make sure virat is a copy, not a view
virat['inning'] = virat['inning'].replace(4, 2)
In [141]:
virat['inning'].value_counts()
Out[141]:
1    3473
2    2763
Name: inning, dtype: int64
In [142]:
virat_inning=virat.groupby('inning')['batsman_runs'].sum().reset_index(name='run')
In [143]:
virat_inning
Out[143]:
inning run
0 1 4400
1 2 3614
In [ ]:
In [144]:
fig=px.bar(

    virat_inning,
    x="inning",
    y="run",
    
     title="1st vs 2nd innings ",
width=400, 
    height=400,
    
   
    labels={"run": "Total Run", "inning": "Innings"}
    
)
fig.update_layout(bargap=.7,
                 template='simple_white')
fig.show()
In [ ]:
In [ ]:

IN INNINGS 2 HE SCORED 3614 RUNS ALMOST HALF OF HIS ENTIRE RUN ,A ABSOLUTE CHASE MASTER¶

In [ ]:
In [ ]:

Q.11E --> KOHLI'S RUNS AGAINST EACH TEAM¶

In [ ]:
In [ ]:
In [145]:
runs=virat.groupby('bowling_team')['batsman_runs'].sum().sort_values(ascending=False).reset_index(name='run').head(5)
In [146]:
runs
Out[146]:
bowling_team run
0 Delhi Capitals 1057
1 Chennai Super Kings 1053
2 Punjab Kings 1030
3 Kolkata Knight Riders 962
4 Mumbai Indians 860
In [147]:
fig=px.bar(

    runs,
    x="bowling_team",
    y="run",
    
     title="RUN againest different Team",
width=600, 
    height=400,
    color="bowling_team",
   
    labels={"run": "Total Run", "bowling_team": "RUN againest different team"}
    
)
fig.update_layout(bargap=.7)
fig.show()
In [ ]:
In [ ]:

FROM THIS WE CAN SAY THAT HIS FAVOURITE OPPONENTS ARE Delhi Capitals (1057 RUNS) , Chennai Super Kings (1053 RUNS), Punjab Kings (1030 RUNS)¶

In [ ]:
In [ ]:

Q.11F --> HIS FAVOURITE VENUE¶

In [ ]:
In [ ]:
In [148]:
stadium_data_virat=df2[['id','season','venue']].merge(virat,left_on='id',right_on='match_id',how='left').drop('match_id',axis=1)
In [149]:
stadium_data=stadium_data_virat.groupby('venue')['batsman_runs'].sum().sort_values(ascending=False).reset_index(name='run').head(5)
In [150]:
stadium_data
Out[150]:
venue run
0 M Chinnaswamy Stadium 3040.0
1 Wankhede Stadium 574.0
2 Rajiv Gandhi International Stadium 491.0
3 Arun Jaitley Stadium 483.0
4 MA Chidambaram Stadium 383.0
In [151]:
fig=px.bar(

    stadium_data,
    x="venue",
    y="run",
    
     title="RUN in different stadium",
width=600, 
    height=400,
    color="venue",
   
    labels={"run": "Total Run", "venue": "RUN in different venue"}
    
)
fig.update_layout(bargap=.7,
                 showlegend=False)
fig.show()
In [ ]:
In [ ]:

OF COURSE M Chinnaswamy Stadium WITH 3040 RUNS¶

In [ ]:
In [ ]:

Q.11G --> KING'S RUNS BY EACH SEASON¶

In [ ]:
In [ ]:
In [152]:
data_virat=df2[['id','season','venue']].merge(virat,left_on='id',right_on='match_id',how='left').drop('match_id',axis=1)
In [153]:
runs_trend=data_virat.groupby('season',as_index=False)['batsman_runs'].sum().sort_values(by='season')
In [154]:
runs_trend['batsman_runs'] = runs_trend['batsman_runs'].astype(int)
runs_trend
Out[154]:
season batsman_runs
0 2007/08 165
1 2009 246
2 2009/10 307
3 2011 557
4 2012 364
5 2013 639
6 2014 359
7 2015 505
8 2016 973
9 2017 308
10 2018 530
11 2019 464
12 2020/21 471
13 2021 405
14 2022 341
15 2023 639
16 2024 741
In [155]:
fig=go.Figure()
fig.add_trace(
    go.Scatter(
        x=runs_trend['season'],
        y=runs_trend['batsman_runs'],
        mode='lines+markers'
    )

)

fig.update_layout(
    xaxis_title='season',
    yaxis_title='total_runs',
    title='total run in each ipl season',
    template='simple_white'
)

fig.show()
In [ ]:
In [ ]:

CONCLUSION --- KING'S PICK YEAR WAS 2016 WITH 973 RECORD BRACKING RUN BUT AFTER THAT THERE WAS A DOWNFALL, BUT THEN THE LAST TWO YEARS HE CAME BACK SUPREMLY 2023 WITH 639 RUNS ,2024 WITH 741 RUNS, ORANGE CUP WINNER¶

In [ ]:
In [ ]:

Q.12 ---> HIGHEST WICKET TAKER¶

In [ ]:
In [ ]:
In [156]:
df1['dismissal_kind'].unique()
Out[156]:
array([nan, 'caught', 'bowled', 'run out', 'lbw', 'retired hurt',
       'stumped', 'caught and bowled', 'hit wicket',
       'obstructing the field', 'retired out'], dtype=object)
In [157]:
dismissal_kinds=['caught', 'bowled','lbw','stumped', 'caught and bowled', 'hit wicket']
In [158]:
bowl=df1[df1['dismissal_kind'] .isin (dismissal_kinds)]
In [159]:
hwtb=bowl['bowler'].value_counts().reset_index().head(10)
In [160]:
hwtb.columns=['bowler','wicket']
hwtb
Out[160]:
bowler wicket
0 YS Chahal 205
1 PP Chawla 192
2 DJ Bravo 183
3 B Kumar 181
4 SP Narine 180
5 R Ashwin 180
6 A Mishra 174
7 SL Malinga 170
8 JJ Bumrah 168
9 RA Jadeja 160
In [161]:
colors=['crimson']+['turquoise']*(len(hwtb)-1)
fig=go.Figure(
data=[
  go.Bar( x=hwtb['bowler'],y=hwtb['wicket'],marker_color=colors,width=0.2)
])

fig.update_layout(

    title='Highest wicket taker',
    xaxis_title='Bowler',
    yaxis_title='Total Wicket',
    bargap=.9,
    width=600,
    template='simple_white'
    
)
fig.show()
In [ ]:
In [ ]:

CONCLUSION --- YS Chahal HOLDS THE HIGHEST WICKET TAKER WITH 205 WICKET, FOLLOWED PP Chawla WITH 192 WICKET¶

In [ ]:
In [ ]:

Q.13 ---> INDIVIDUAL BOWLER PERFROMANCE ANALYSIS . I CHOOSE YS Chahal¶

In [ ]:
In [ ]:

DISMISSAL TYPE¶

In [ ]:
In [162]:
dismissal_kinds=['caught', 'bowled','lbw','stumped', 'caught and bowled', 'hit wicket']
In [163]:
bowl=df1[df1['dismissal_kind'] .isin (dismissal_kinds)]
In [164]:
chahal=bowl[bowl['bowler']=='YS Chahal']
In [165]:
c=chahal.groupby('dismissal_kind')['is_wicket'].sum().sort_values(ascending=False).reset_index(name='wicket')
In [ ]:
In [166]:
c.style.background_gradient(cmap='Purples')
Out[166]:
  dismissal_kind wicket
0 caught 127
1 bowled 36
2 stumped 20
3 lbw 18
4 caught and bowled 4

CONCLUSION --- MOST NUMBER OF TIME ,127 TIMES HE TOOK WICKET BY CAUGHT¶

In [ ]:
In [ ]:
In [ ]:

FAVOURITE VENUE¶

In [ ]:
In [ ]:
In [167]:
data_chahal=df2[['id','season','venue']].merge(chahal,left_on='id',right_on='match_id',how='left').drop('match_id',axis=1)
In [168]:
cw=data_chahal.groupby('venue')['is_wicket'].sum().sort_values(ascending=False).reset_index(name='wicket').head(5)
In [169]:
cw['wicket'] = cw['wicket'].astype(int)
In [170]:
cw.style.background_gradient(cmap='Greens')
Out[170]:
  venue wicket
0 M Chinnaswamy Stadium 52
1 Wankhede Stadium 20
2 Dubai International Cricket Stadium 19
3 Sawai Mansingh Stadium 15
4 Sharjah Cricket Stadium 12
In [ ]:
In [ ]:

HIS FAVOURITE VENUE IS M Chinnaswamy Stadium WHERE HE TOOK 52 WICKET ,FOLLOWED BY Wankhede Stadium 20 WICKET¶

In [ ]:
In [ ]:

WHEN HE IS EFFECTIVE¶

In [ ]:
In [ ]:
In [171]:
c=chahal.groupby('over',as_index=False)['is_wicket'].sum().sort_values(by=['over','is_wicket'],ascending=[True,False])
In [172]:
c.style.background_gradient(cmap='Reds')
Out[172]:
  over is_wicket
0 0 1
1 1 2
2 2 2
3 3 3
4 4 3
5 5 6
6 6 17
7 7 14
8 8 13
9 9 12
10 10 13
11 11 12
12 12 16
13 13 17
14 14 15
15 15 28
16 16 13
17 17 11
18 18 6
19 19 1
In [ ]:
In [ ]:

CONCLUSION --- HE TOOK 28 WICKETS AT 15'TH OVER ,BETWEEN 6 TO 17 OVERS HE TOOKES MOST OF HIS WICKETS SO HE IS EFFECTIVE IN MIDDLE OVERS¶

In [ ]:
In [ ]:

chahal WICKET PER SEASON¶

In [ ]:
In [ ]:
In [173]:
c_trend=data_chahal.groupby('season',as_index=False)['is_wicket'].sum().sort_values(by='is_wicket',ascending=False)
In [174]:
c_trend['is_wicket'] = c_trend['is_wicket'].astype(int)
In [175]:
fig=go.Figure()
fig.add_trace(
    go.Scatter(
        x=c_trend['season'],
        y=c_trend['is_wicket'],
        mode='lines+markers'
    )

)

fig.update_layout(
    xaxis_title='season',
    yaxis_title='total_wicket',
    title='total wicket in each ipl season',
    template='simple_white'
)

fig.show()
In [ ]:
In [ ]:

HE TOOK 27 WICKET IN 2022¶

In [ ]:
In [ ]:

TOTAL RUNS VS TOTAL WICKET PER SEASON¶

In [ ]:
In [ ]:
In [176]:
c_trend_run=df1[df1['bowler']=='YS Chahal']
data_chahal_run=df2[['id','season','venue']].merge(c_trend_run,left_on='id',right_on='match_id',how='left').drop('match_id',axis=1)
In [177]:
c_trend_run_season=data_chahal_run.groupby('season',as_index=False)['total_runs'].sum().sort_values(by='total_runs',ascending=False)
In [178]:
c_trend_run_season['total_runs']=c_trend_run_season['total_runs'].astype(int)
In [179]:
c_trend_run_season['total_wicket']=c_trend['is_wicket']
c_trend_run_season['total_wicket'] = c_trend_run_season['total_wicket'].fillna(0).astype(int)
In [ ]:
In [180]:
c_run_vs_wicket=c_trend_run_season.reset_index()
In [181]:
c_run_vs_wicket.style.background_gradient(cmap='Greens')
Out[181]:
  index season total_runs total_wicket
0 16 2024 548 18
1 14 2022 536 27
2 15 2023 434 21
3 7 2015 416 23
4 12 2020/21 414 21
5 8 2016 409 21
6 11 2019 394 18
7 6 2014 389 12
8 13 2021 381 18
9 10 2018 374 12
10 9 2017 351 14
11 5 2013 35 0
12 1 2009 0 0
13 4 2012 0 0
14 3 2011 0 0
15 2 2009/10 0 0
16 0 2007/08 0 0
In [ ]:
In [ ]:

THIS TABLE SHOWS TOTAL RUNS AND TOTAL WICKET PER SEASON¶

In [ ]:
In [ ]:

HIS FAVOURITE BATSMAN¶

In [ ]:
In [ ]:
In [182]:
cb=chahal.groupby('batter')['is_wicket'].sum().sort_values(ascending=False).reset_index(name='wicket')
In [183]:
cb=cb.head(5)
cb
Out[183]:
batter wicket
0 MA Agarwal 6
1 N Rana 6
2 Q de Kock 6
3 SV Samson 5
4 AT Rayudu 4
In [184]:
fig=px.bar(
    cb,
    x="batter",
    y="wicket",
    
     title="Chahal favourite batsman",
     width=600, 
    height=400,
    color="batter",
   
    labels={"wicket": "Total out", "batter": "Batsman"}
    
)
fig.update_layout(bargap=.7,
                 showlegend=False)
fig.show()
In [ ]:
In [ ]:

MA Agarwal GOT OUT 6 TIMES ,FOLLOWED BY N Rana 6 TIMES BY CHAHAL¶

In [ ]:
In [ ]:

Q.14 INNINGS WISE RUN ANALYSIS BY TEAM¶

In [ ]:

FIRST INNINGS¶

In [185]:
run=df1.groupby(['match_id','inning','batting_team'])['total_runs'].sum().reset_index()
In [186]:
inning1=run[run['inning']==1]
inning2=run[run['inning']==2]
In [187]:
fig=px.box(data_frame=inning1,x='batting_team',y='total_runs',color='batting_team',
          width=800,
          height=500)
fig.update_layout(title='Batting first',showlegend=False)
fig.show()
In [ ]:
In [ ]:

CONCLUSION --- WHILE BATTING FIRST RCB , KKR , DC , SRH , LSG HAD 250+ SCORED ALSO WHILE BATTING FIRST KKR , RCB , DC HAD SCORED LESS THAN 70 RUNS¶

In [ ]:
In [ ]:

SECOND INNINGS¶

In [188]:
fig=px.box(data_frame=inning2,x='batting_team',y='total_runs',color='batting_team',
          width=800,
          height=500)
fig.update_layout(title='Batting Second',showlegend=False)
fig.show()
In [ ]:
In [ ]:

CONCLUSION --- WHILE BOWLING RCB , PBKS THEY BOTH GOT 250 + RUNS¶

In [ ]:
In [ ]:
In [ ]:

Q.16 MOST 200+ RUNS BY A BATTNG TEAM AND BOWLING TEAM¶

In [ ]:

BATTING TEAM¶

In [ ]:
In [189]:
score=df1.groupby(['match_id','inning','batting_team','bowling_team'])['total_runs'].sum().reset_index()
In [190]:
score_200=score[score['total_runs']>=200]
In [191]:
score_200
Out[191]:
match_id inning batting_team bowling_team total_runs
0 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 222
2 335983 1 Chennai Super Kings Punjab Kings 240
3 335983 2 Punjab Kings Chennai Super Kings 207
14 335989 1 Chennai Super Kings Mumbai Indians 208
15 335989 2 Mumbai Indians Chennai Super Kings 202
... ... ... ... ... ...
2199 1426302 1 Delhi Capitals Lucknow Super Giants 208
2203 1426305 1 Lucknow Super Giants Mumbai Indians 214
2205 1426306 1 Royal Challengers Bangalore Chennai Super Kings 218
2207 1426307 1 Punjab Kings Sunrisers Hyderabad 214
2208 1426307 2 Sunrisers Hyderabad Punjab Kings 215

211 rows × 5 columns

In [192]:
bat=score_200['batting_team'].value_counts().reset_index().head(5)
In [193]:
bat.columns=['Team','Total']
bat
Out[193]:
Team Total
0 Chennai Super Kings 32
1 Royal Challengers Bangalore 30
2 Kolkata Knight Riders 25
3 Mumbai Indians 25
4 Punjab Kings 24
In [194]:
colors=['crimson']+['turquoise']*(len(bat)-1)
fig=go.Figure(
data=[
  go.Bar( x=bat['Team'],y=bat['Total'],marker_color=colors,width=0.2)
])

fig.update_layout(

    title='Most 200+ scores by a batting team',
    xaxis_title='Team',
    yaxis_title='Total',
    bargap=.5,
    width=600,
    height=400
    
)
fig.show()
In [ ]:
In [ ]:

CONCLUSION -- CSK ACHIEVED MOST 200+ RUNS WHILE BATTING 32 TIMES FOLLOWED BY RCB 30 TIMS¶

In [ ]:
In [ ]:

BOWLING TEAM¶

In [ ]:
In [195]:
bowl=score_200['bowling_team'].value_counts().reset_index().head(5)
In [196]:
bowl.columns=['Team','Total']
bowl
Out[196]:
Team Total
0 Punjab Kings 30
1 Royal Challengers Bangalore 28
2 Chennai Super Kings 23
3 Delhi Capitals 23
4 Sunrisers Hyderabad 23
In [197]:
colors=['crimson']+['turquoise']*(len(bat)-1)
fig=go.Figure(
data=[
  go.Bar( x=bowl['Team'],y=bowl['Total'],marker_color=colors,width=0.2)
])

fig.update_layout(

    title='Most 200+ scores by a bowling team',
    xaxis_title='Team',
    yaxis_title='Total',
    bargap=.5,
    width=600,
    height=400
    
)
fig.show()
In [ ]:
In [ ]:

CONCLUSION --- WHILE BOWLING PBKS HAD ACHIEVED 30 TIMES 200 + RUNS FOLLOWED BY RCB 28 TIMES¶

In [ ]:
In [ ]:

Q.17 WIN A MATCH WITH MAXIMUN RESULT MARGIN¶

In [ ]:
In [198]:
df2[df2['result_margin']==df2['result_margin'].max()]
Out[198]:
id season city date match_type player_of_match venue team1 team2 toss_winner ... winner result result_margin target_runs target_overs super_over method umpire1 umpire2 toss_winner_won
620 1082635 2017 Delhi 2017-05-06 League LMP Simmons Arun Jaitley Stadium Delhi Capitals Mumbai Indians Delhi Capitals ... Mumbai Indians runs 146.0 213.0 20.0 N No method applied Nitin Menon CK Nandan False

1 rows × 21 columns

In [ ]:

CONCLUSION ---IN 2017 MI VS DC ,MI WON BY A HUGE MARGIN BY 146 RUNS¶

In [ ]:
In [ ]:

Q.18 TEAMS WITH MORE THAN 100+ RESULT MARGIN¶

In [ ]:
In [ ]:
In [199]:
matches_with_large_margin = df2[df2['result_margin'] > 100]
head_10_large_margin = matches_with_large_margin[['winner', 'team1','team2','result_margin']].head(12)
head_10_large_margin['loss_side'] = head_10_large_margin.apply(
    lambda row: row['team2'] if row['winner'] == row['team1'] else row['team1'], axis=1
)

# Display 'loss_side' and 'result_margin'
result = head_10_large_margin[['winner','loss_side', 'result_margin']].reset_index()
In [200]:
result
Out[200]:
index winner loss_side result_margin
0 0 Kolkata Knight Riders Royal Challengers Bangalore 140.0
1 55 Rajasthan Royals Delhi Capitals 105.0
2 236 Punjab Kings Royal Challengers Bangalore 111.0
3 352 Royal Challengers Bangalore Pune Warriors 130.0
4 496 Royal Challengers Bangalore Punjab Kings 138.0
5 560 Royal Challengers Bangalore Gujarat Lions 144.0
6 620 Mumbai Indians Delhi Capitals 146.0
7 676 Mumbai Indians Kolkata Knight Riders 102.0
8 706 Sunrisers Hyderabad Royal Challengers Bangalore 118.0
9 1009 Royal Challengers Bangalore Rajasthan Royals 112.0
10 1039 Kolkata Knight Riders Delhi Capitals 106.0
In [ ]:
In [ ]:

CONCLUSION -- RCB WIN 4 TIMES WITH 100+ RUNS ALSO LOSS 3 TIME WITH 100+¶

In [ ]:
In [ ]:

Q.19 BATSMAN WITH MOST NUMBER OF BALL PLAYED¶

In [ ]:
In [ ]:
In [201]:
highest_ball_played=df1.groupby('batter',as_index=False)['ball'].count().sort_values(by='ball',ascending=False).reset_index(drop='index').head(10)
In [202]:
highest_ball_played.style.background_gradient(cmap='PuBu')
Out[202]:
  batter ball
0 V Kohli 6236
1 S Dhawan 5483
2 RG Sharma 5183
3 DA Warner 4849
4 SK Raina 4177
5 MS Dhoni 3947
6 RV Uthappa 3927
7 AM Rahane 3858
8 KD Karthik 3687
9 KL Rahul 3578
In [ ]:
In [ ]:

CONCLUSION --- CLEARLY KING KOHLI PLAYED MOST BALLS 6326 BALLS, FOLLOWED BY S Dhawan 5483 BALLS,RG Sharma 5183 BALLS¶

In [ ]:
In [ ]:

Q.20 MOST NUMBER OF 6'S¶

In [ ]:
In [ ]:
In [ ]:
In [203]:
six=df1.groupby('batter')['batsman_runs'].agg(lambda x: (x==6).sum()).reset_index()
six.columns=['batter','6s']
six=six.sort_values(by='6s',ascending=False).head(10).reset_index(drop='index')
six.style.background_gradient(cmap='Greens')
Out[203]:
  batter 6s
0 CH Gayle 359
1 RG Sharma 281
2 V Kohli 273
3 AB de Villiers 253
4 MS Dhoni 252
5 DA Warner 236
6 KA Pollard 224
7 AD Russell 209
8 SV Samson 206
9 SK Raina 204
In [ ]:
In [ ]:

CONCLUSION -- UNIVERSE BOSS CONQUER WITH 359 SIXS, RG Sharma 281 SIXS, FOLLOWED BY KOHLI 273 SIXS¶

In [ ]:

Q.21 MOST NUMBER OF 4'S¶

In [ ]:
In [ ]:
In [204]:
six=df1.groupby('batter')['batsman_runs'].agg(lambda x: (x==4).sum()).reset_index()
six.columns=['batter','4s']
six=six.sort_values(by='4s',ascending=False).head(10).reset_index(drop='index')
six.style.background_gradient(cmap='coolwarm')
Out[204]:
  batter 4s
0 S Dhawan 768
1 V Kohli 708
2 DA Warner 663
3 RG Sharma 599
4 SK Raina 506
5 G Gambhir 492
6 RV Uthappa 481
7 AM Rahane 479
8 KD Karthik 466
9 F du Plessis 422
In [ ]:

CONCLUSION S Dhawan HITS 768,V Kohli 708 AND FOLLOWED BY DA Warner 663 FOURS¶

In [ ]:
In [ ]:

Q.22 WICKET KEEPER WITH MOST NUMBER OF STUMPED¶

In [ ]:
In [ ]:
In [205]:
dismissal_kindss=['stumped']
In [206]:
keeper=df1[df1['dismissal_kind'] .isin (dismissal_kindss)]
In [207]:
keeper['dismissal_kind'].value_counts()
Out[207]:
stumped    358
Name: dismissal_kind, dtype: int64
In [208]:
wicket_keeper=keeper.groupby(['fielder', 'dismissal_kind']).size().reset_index(name='count')
In [209]:
wicket_keeper=wicket_keeper.sort_values(by='count',ascending=False).head(10).reset_index(drop='index')
In [210]:
wicket_keeper.style.background_gradient(cmap='winter')
Out[210]:
  fielder dismissal_kind count
0 MS Dhoni stumped 42
1 KD Karthik stumped 37
2 RV Uthappa stumped 32
3 WP Saha stumped 26
4 RR Pant stumped 23
5 PA Patel stumped 16
6 Q de Kock stumped 16
7 AC Gilchrist stumped 16
8 SV Samson stumped 16
9 NV Ojha stumped 10
In [ ]:

CONCLUSION -- MS Dhoni stumped 42 ,FOLLOWED BY KD Karthik stumped 37,AND RV Uthappa stumped 32 ALL THREE INDIAN¶

In [ ]:
In [ ]:

Q.23 HIGHEST STRIKE RATE DURING DEATH OVER¶

In [ ]:
In [ ]:
In [ ]:
In [211]:
valid_deliveries = df1[~df1['extras_type'].isin(['wides','byes','penalty'])]
In [212]:
df_filtered = valid_deliveries[(valid_deliveries['over'] >= 16) & (valid_deliveries['over'] <= 20)]
In [213]:
# Recalculate ball count faced by each batter
ball_face_count = df_filtered.groupby('batter').size().reset_index(name='ball_face')

# Recalculate total runs scored by each batter
total_runs_count = df_filtered.groupby('batter')['batsman_runs'].sum().reset_index(name='total_runs')
batter_stats = pd.merge(total_runs_count, ball_face_count, on='batter', how='inner')
In [214]:
filtered_batters=batter_stats[batter_stats['ball_face'] > 250]
filtered_batters = filtered_batters.copy()

filtered_batters['strike_rate'] = (filtered_batters['total_runs'] / filtered_batters['ball_face']) * 100
batter_stats_over_100 = filtered_batters.sort_values(by='strike_rate',ascending=False).head(10)
In [215]:
batter_death_over = batter_stats_over_100.reset_index(drop='index')
In [216]:
batter_death_over.style.background_gradient(cmap='winter')
Out[216]:
  batter total_runs ball_face strike_rate
0 AB de Villiers 1421 607 234.102142
1 RR Pant 626 302 207.284768
2 AD Russell 1065 521 204.414587
3 V Kohli 1099 545 201.651376
4 SO Hetmyer 680 342 198.830409
5 RG Sharma 1176 598 196.655518
6 SV Samson 547 280 195.357143
7 SA Yadav 516 269 191.821561
8 MS Dhoni 2786 1467 189.911384
9 KD Karthik 1565 834 187.649880
In [ ]:

conclusion --- AB de Villiers with 1421 runs 607 ball played and 234.10 STRIKE RATE HOLD THE RECORD ,WHILE DHONI DURING DEATH OVER SCORED 2786 RUNS¶

In [ ]:
In [ ]:

Q.24 MOST MOM PLAYER¶

In [ ]:
In [217]:
man_of_the_match=df2['player_of_match'].value_counts().head(10).reset_index()
In [218]:
man_of_the_match.columns=['player','MoM']
In [219]:
man_of_the_match.style.background_gradient(cmap='PuBu')
Out[219]:
  player MoM
0 AB de Villiers 25
1 CH Gayle 22
2 RG Sharma 19
3 DA Warner 18
4 V Kohli 18
5 MS Dhoni 17
6 SR Watson 16
7 YK Pathan 16
8 RA Jadeja 16
9 AD Russell 15
In [ ]:
In [ ]:
In [220]:
colors=['crimson']+['turquoise']*(len(man_of_the_match)-1)
fig=go.Figure(
data=[
  go.Bar( x=man_of_the_match['player'],y=man_of_the_match['MoM'],marker_color=colors,width=0.4)
])

fig.update_layout(

    title='Most Man Ot The Match Award',
    xaxis_title='player',
    yaxis_title='Count',
    bargap=.9,
    width=600,
    height=400
    
)
fig.show()
In [ ]:
In [ ]:

CONCLUSION --- AB de Villiers WITH 25,CH Gayle WITH 22,FOLLOWED BY RG Sharma WITH 19 MOM AWARD ARE THE TOP THREE¶

In [ ]:
In [ ]:

Q.25 BEST RUN RATE DURING POWER PLAY AND DEATH OVER (2024)¶

In [ ]:
In [ ]:

DURING POWER PLAY¶

In [ ]:
In [221]:
powerplay=df2[['id','season','venue']].merge(df1,left_on='id',right_on='match_id',how='left').drop('match_id',axis=1)
In [222]:
powerplay_data_2024 = powerplay[(powerplay['season'] == '2024') & (powerplay['over'] >= 0) & (powerplay['over'] <= 5)]
In [223]:
powerplay_data=powerplay_data_2024.groupby('batting_team').agg(
    total_runs=('total_runs','sum'),
    ball_face=('ball','count')
).sort_values(by='total_runs',ascending=False)
powerplay_data
Out[223]:
total_runs ball_face
batting_team
Sunrisers Hyderabad 1073 605
Kolkata Knight Riders 930 531
Delhi Capitals 897 531
Royal Challengers Bangalore 876 552
Mumbai Indians 789 526
Rajasthan Royals 773 564
Punjab Kings 743 519
Chennai Super Kings 733 521
Lucknow Super Giants 695 524
Gujarat Titans 556 450
In [ ]:
In [224]:
extra_balls_data1 = powerplay_data_2024[powerplay_data_2024['extras_type'].notnull() & 
    ~powerplay_data_2024['extras_type'].isin(['byes','legbyes'])]

extra_balls_count1 = extra_balls_data1.groupby('batting_team').agg(
    extra_balls=('extras_type','count')
).sort_values(by='extra_balls', ascending=False)
In [225]:
extra_balls_count1
Out[225]:
extra_balls
batting_team
Sunrisers Hyderabad 29
Delhi Capitals 26
Kolkata Knight Riders 26
Rajasthan Royals 24
Mumbai Indians 21
Lucknow Super Giants 20
Chennai Super Kings 17
Gujarat Titans 17
Punjab Kings 15
Royal Challengers Bangalore 12
In [226]:
powerplay_data['ActualBall_Face']=powerplay_data['ball_face']-extra_balls_count1['extra_balls']
In [227]:
powerplay_data=powerplay_data.drop('ball_face',axis=1)
In [228]:
powerplay_data
Out[228]:
total_runs ActualBall_Face
batting_team
Sunrisers Hyderabad 1073 576
Kolkata Knight Riders 930 505
Delhi Capitals 897 505
Royal Challengers Bangalore 876 540
Mumbai Indians 789 505
Rajasthan Royals 773 540
Punjab Kings 743 504
Chennai Super Kings 733 504
Lucknow Super Giants 695 504
Gujarat Titans 556 433
In [229]:
powerplay_data['RR']=round(((powerplay_data['total_runs']/powerplay_data['ActualBall_Face'])*6),2)
In [230]:
powerplay_data=powerplay_data.sort_values(by='RR',ascending=False)
In [231]:
powerplay_data.style.background_gradient(cmap='Greens')
Out[231]:
  total_runs ActualBall_Face RR
batting_team      
Sunrisers Hyderabad 1073 576 11.180000
Kolkata Knight Riders 930 505 11.050000
Delhi Capitals 897 505 10.660000
Royal Challengers Bangalore 876 540 9.730000
Mumbai Indians 789 505 9.370000
Punjab Kings 743 504 8.850000
Chennai Super Kings 733 504 8.730000
Rajasthan Royals 773 540 8.590000
Lucknow Super Giants 695 504 8.270000
Gujarat Titans 556 433 7.700000
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CONCLUSION --- SRH , KKR , DC BOTH THE TEAM HAVE 10+ RUN RATE DURING POWER PLAY¶

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DURING DEATH OVER¶

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In [232]:
death=df2[['id','season','venue']].merge(df1,left_on='id',right_on='match_id',how='left').drop('match_id',axis=1)
In [233]:
deathover_data_2024 = death[(death['season'] == '2024') & (death['over'] >= 16) & (death['over'] <= 20)]
# valid_deliveries = powerplay_data_2024[powerplay_data_2024['extras_type'].isin(['wide', 'byes', 'legbyes'])]
In [234]:
deathover_data=deathover_data_2024.groupby('batting_team').agg(
    total_runs=('total_runs','sum'),
    ball_face=('ball','count')
).sort_values(by='total_runs',ascending=False)
deathover_data
Out[234]:
total_runs ball_face
batting_team
Sunrisers Hyderabad 615 348
Royal Challengers Bangalore 611 338
Chennai Super Kings 608 332
Rajasthan Royals 560 329
Lucknow Super Giants 531 320
Delhi Capitals 523 289
Mumbai Indians 514 291
Punjab Kings 514 306
Gujarat Titans 465 275
Kolkata Knight Riders 432 225
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In [235]:
extra_balls_data = deathover_data_2024[deathover_data_2024['extras_type'].notnull() & 
    ~deathover_data_2024['extras_type'].isin(['byes','legbyes'])]
extra_balls_count = extra_balls_data.groupby('batting_team').agg(
    extra_balls=('extras_type', 'count')
).sort_values(by='extra_balls', ascending=False)
In [236]:
extra_balls_count
Out[236]:
extra_balls
batting_team
Royal Challengers Bangalore 32
Chennai Super Kings 28
Mumbai Indians 26
Punjab Kings 26
Kolkata Knight Riders 25
Sunrisers Hyderabad 23
Gujarat Titans 22
Lucknow Super Giants 20
Rajasthan Royals 19
Delhi Capitals 14
In [237]:
deathover_data['ActualBall_Face']=deathover_data['ball_face']-extra_balls_count['extra_balls']
In [238]:
deathover_data=deathover_data.drop('ball_face',axis=1)
In [239]:
deathover_data
Out[239]:
total_runs ActualBall_Face
batting_team
Sunrisers Hyderabad 615 325
Royal Challengers Bangalore 611 306
Chennai Super Kings 608 304
Rajasthan Royals 560 310
Lucknow Super Giants 531 300
Delhi Capitals 523 275
Mumbai Indians 514 265
Punjab Kings 514 280
Gujarat Titans 465 253
Kolkata Knight Riders 432 200
In [240]:
deathover_data['RR']=round(((deathover_data['total_runs']/deathover_data['ActualBall_Face'])*6),2)
In [241]:
deathover_data=deathover_data.sort_values(by='RR',ascending=False)
In [242]:
deathover_data.style.background_gradient(cmap='winter')
Out[242]:
  total_runs ActualBall_Face RR
batting_team      
Kolkata Knight Riders 432 200 12.960000
Chennai Super Kings 608 304 12.000000
Royal Challengers Bangalore 611 306 11.980000
Mumbai Indians 514 265 11.640000
Delhi Capitals 523 275 11.410000
Sunrisers Hyderabad 615 325 11.350000
Gujarat Titans 465 253 11.030000
Punjab Kings 514 280 11.010000
Rajasthan Royals 560 310 10.840000
Lucknow Super Giants 531 300 10.620000
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KKR HAVE A RUN RATE ABOUT 13 DURING DEATH OVER WHERE SRH SCORED 615 RUN WITH A 11.13 RUN RATE DURING DEATH OVER¶

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